Subject
Business Intelligence
| Subject | Business Intelligence |
| Subject code | COM535 |
| Contact | Andrea Kő |
| Department | Department of Information Systems |
| Level | G |
| Lectures | 2 |
| Seminars | 2 |
| Credit | |
| Prerequisities | There are no special requirements |
| Office hours | Tuesday 11.30-12.30 |
| Classes | Monday 9.50-11.20. 11.40-13.10 |
| Description | Today’s advanced information and communication technology, data produced in a large volume enable executives to use information in radically new ways, to make dramatically more effective decisions -- and make those decisions more rapidly. Business intelligence course offers a comprehensive overview about business intelligence area, and how it can be used for effective and efficient decision making. Topics include: business analytics, business performance management, data mining and web mining. Course will also cover the following areas: data warehousing, including access, analysis, visualization, modeling, and support. Each significant new technology will be introduced, and working mechanism is demonstrated. In many cases practical guidance on integrating it into real-world organizations is showed. Examples, products, services, and exercises are presented throughout. Course will highlight recent innovations of the field, like web 2.0 tools, predictive analytics, business performance analysis, big data management. Real business scenarios for the use of advanced management support technology are presented. The course is supported by a web site containing additional readings, relevant links, and other supplements. |
| Program | Detailed class schedule: Date of class Topics to be discussed, readings required for the class Week 1 02.03. Foundation of business intelligence. Topics covered during the course, students’ expectations, course requirements, and individual presentations How to use CooSpace System Reading Chapter 1. Week 2 02.10. Decision Support and Business Intelligence: Concepts, Methodologies; Technologies: An Overview Reading Chapter 2. & 3 Week 3 02.17. Business intelligence: data management, data acquisition, ETL & Data warehousing Reading Chapter 5. Week 4. 02.24. Business analytics Reading Chapter 6. Week 5. 03.03. Tableau lab work Week 6 03.10. Tableau lab work Week 7 03.17. Session 1:Tableau lab work Session 2: Midterm exam Week 8 03.24. Tableau lab work Reading Chapter 6. Week 9 03. 31. Data mining Reading Chapter 7. Week 10 04.07. Web mining and text mining Reading Chapter 7 Data mining lab work Week 11 04.14. Text mining Reading Chapter 7. Data mining lab work Week 12 04.21. Eastern Holiday Week 13 04.28. Data mining lab work Week 14 05.05. Recent innovation in BI: big data management, complex business intelligent systems Week 15 Final exam Week 16 Make-up exam |
| Course materials | Compulsory reading: Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence Systems, 9/E, 2011 (ISBN-10: 013610729X). Recommended readings: Liu, B. Web Data Mining, 2011, Springer second ed. ISBN 978-3-642-19459-7 (http://www.cs.uic.edu/~liub/WebMiningBook.html) Turban, E., Aronson, King, D., J. E., Sharda, R.: Business Intelligence, Prentice Hall, 2008 (ISBN-10: 013234761X, ISBN-13: 9780132347617) The eLearning site of the course will be available at: http://coo.uni-corvinus.hu/coospace |
| Course requirements and grading | Assignments: The major part of the classes will be based on individual or group problem solving. Students have to participate in computer lab work (assignment or team work) and based on case studies they have to write reports, prepare short assignments (papers with 2-4- pages) during (and after) the classes. Internet exercises and documented class work will be also evaluated. Exams The midterm and the final exam are written exams each lasting for 80 minutes. Both consist of 10 multiple choice test questions (worth each 1 point) and 4 essay questions (worth each 5 points). Each point equals 1 percent of the final grade. Assessment, grading: Grading 25% mid-term exam 25% final exam 50% assignment |
